Presentation on theme: "Quality Management in POM – Part 2"— Presentation transcript:
1Quality Management in POM – Part 2 IE 3265 R. Lindeke, Ph. D.Quality Management in POM – Part 2
2Topics Managing a Quality System Achieving Quality in a System Total Quality Management (TQM) Achieving Quality in a System Look early and often 6 Sigma – an approach & a technique Make it a part of the process The Customers Voice in Total Quality Management QFD and the House of QualityQuality EngineeringLoss FunctionQuality StudiesExperimental ApproachesT.M.; FMEA; Shainin
3Taguchi’s Loss Function Taguchi defines Quality Level of a product as the Total Loss incurred by society due to failure of a product to perform as desired when it deviates from the delivered target performance levels.This includes costs associated with poor performance, operating costs (which changes as a product ages) and any added expenses due to harmful side effects of the product in use
4Exploring the Taguchi Method Considering the Loss Function, it is quantifiableLarger is Better:Smaller is Better:Nominal is Best:
5Considering the Cost of Loss k in the L(y) equation is found from:
6Loss Function Example: (nominal is best) We can define a processes average loss as:s is process (product) Standard Deviationybar is process (product) mean
7Example cont.A0 is $2 (a very low number of this type!) found by estimating that the loss is 10% of the $20 product cost when a part is exactly 8.55 or 8.45 unitsProcess specification is: unitsHistorically: ybar = and s = 0.016
8Example Cont. Average Loss: If we make 250,000 units a year Annual Loss is $64,000
9Fixing it Shift the Mean to nominal Reduce variation (s = 0.01) Fix Both!
10Taguchi Methods Help companies to perform the Quality Fix! Quality problems are due to Noises in the product or process systemNoise is any undesirable effect that increases variabilityConduct extensive Problem AnalysesEmploy Inter-disciplinary TeamsPerform Designed Experimental AnalysesEvaluate Experiments using ANOVA and Signal-to noise techniques
11Defining the Taguchi Approach – The Point Then Is To Produce Processes Or Products The Are ROBUST AGAINST NOISESDon’t spend the money to eliminate all noise, build designs (product and process) that can perform as desired – low variability – in the presence of noise!WE SAY: ROBUSTNESS = HIGH QUALITY
12Defining the Taguchi Approach – Noise Factors Cause Functional VariationThey Fall Into Three “Classes”1. Outer Noise – Environmental Conditions2. Inner Noise – Lifetime Deterioration3. Between Product Noise – Piece To Piece Variation
14Defining the Taguchi Approach TO RELIABLY MEET OUR DESIGN GOALS MEANS: DESIGNING QUALITY IN!We find that Taguchi considered THREE LEVELS OF DESIGN:level 1: SYSTEM DESIGNlevel 2: PARAMETER DESIGNlevel 3: TOLERANCE DESIGN
15Defining the Taguchi Approach – SYSTEM DESIGN: All About Innovation – New Ideas, Techniques, PhilosophiesApplication Of Science And Engineering KnowledgeIncludes Selection Of:MaterialsProcessesTentative Parameter Values
16Defining the Taguchi Approach – Parameter Design: Tests For Levels Of Parameter ValuesSelects "Best Levels" For Operating Parameters to be Least Sensitive to NoisesDevelops Processes Or Products That Are RobustA Key Step To Increasing Quality Without Increased Cost
17Defining the Taguchi Approach – Tolerance Design: A "Last Resort" Improvement StepIdentifies Parameters Having the greatest Influence On Output VariationTightens Tolerances On These ParametersTypically Means Increases In Cost
18Selecting Parameters for Study and Control Select The Quality CharacteristicDefine The Measurement TechniqueEnnumerate, Consider, And Select The Independent Variables And InteractionsBrainstormingShainin’s technique where they are determined by looking at the productsFMEA – failure mode and effects analysis
19Preliminary Steps in Improvement Studies To Adequately Address The Problem At Hand We Must:1. Understand Its Relationship With The Goals We Are Trying To Achieve2. Explore/Review Past Performance compare to desired Solutions3. Prepare An 80/20 Or Pareto Chart Of These Past Events4. Develop A "Process Control" Chart -- This Helps To Better See The Relationship between Potential Control And Noise FactorsA Wise Person Can Say: A Problem Well Defined Is Already Nearly Solved!!
20Going Down the Improvement Road Start By Generating The Problem Candidates List:Brainstorm The Product Or ProcessDevelop Cause And Effects (Ishikawa) DiagramsUsing Process Flow Charts To Stimulate IdeasDevelop Pareto Charts For Quality Problems
21DEVELOPING A Cause-and-Effect Diagram: 1. Construct A Straight Horizontal Line (Right Facing)2. Write Quality Characteristic At Right3. Draw 45° Lines From Main Horizontal (4 Or 5) For Major Categories: Manpower, Materials, Machines, Methods And Environment4. Add Possible Causes By Connecting Horizontal Lines To 45° "Main Cause" Rays5. Add More Detailed Potential Causes Using Angled Rays To Horizontal Possible Cause Lines
23Building the ‘Experiment’ Working From a Cause & Effect Diagram
24Designing A Useful Experiment Taguchi methods use a cookbook approach!! Building Experiments for selected factors on the C&E DiagramSelection is from a discrete set of ‘Orthogonal Arrays’Note: an orthogonal array (OA) is a special fractional factorial design that allows study of main factors and 2-way interactions
25T.M. SummaryTaguchi methods (TM) are product or process improvement techniques that use DOE methods for improvementsA set of cookbook designs are available – and they can be modified to build a rich set of studies (beyond what we have seen in MP labs!)TM requires a commitment to complete studies and the discipline to continue in the face of setbacks (as do all quality improvement methods!)
26Simplified DOEShainin Tools – these are a series of steps to logically identify the root causes of variationThese tools are simple to implement, statistically powerful and practicalInitial Step is to sample product (over time) and examine the sample lots for variability to identify causative factors – this step is called the multi-vari chart approachShainin refers to root cause factors as the “Red X”, “Pink X”, and “Pink-Pink X” causes
27Shainin’s ‘Experimental Approaches’ to Quality Variability Control:
28Shainin Ideas – exploring further Red X – the primary cause of variationPink X – the secondary causes of variationPink-Pink X significant but minor causes of variation (a factor that still must be controlled!)Any other factors should be substituted by lower cost solutions (wider tolerance, cheaper material, etc.)
29Basis of Shainin’s Quality Improvement Approaches As Shainin Said: “Don’t ask the engineers, they don’t know, ask the parts”Contrast with Brainstorming approach of Taguchi MethodMulti-Vari is designed to identify the likely home of the Red X factors – not necessarily the factors themselvesShainin suggests that we look into three source of variation regimes:PositionalCyclicalTemporal
30Does the mean shift in time or between products or is the product (alone) showing the variability?
31Positional Variations: These are variation within a given unit (of production)Like porosity in castings – or cracksOr across a unit with many parts – like a transmission, turbine or circuit boardCould be variations by location in batch loading processesCavity to cavity variation in plastic injection molding, etc.Various tele-marketers at a fund raiserVariation from machine-to-machine, person-to-person or plant-to-plant
32Cyclical VariationVariation between consecutive units drawn from a process (consider calls on a software help line)Variation AMONG groups of unitsBatch-to Batch VariationsLot-to-lot variations
33Temporal Variations Variations from hour-to-hour Variation shift-to-shiftVariations from day-to-dayVariation from week-to-week
34Components Search – the prerequisites The technique is applicable (primarily) in ass’bly operations where good units and bad units are foundPerformance (output) must be measurable and repeatableUnits must be capable of disassembly and reassembly without significant change in original performanceThere must be at least 2 assemblies or units – one good, one bad
35The procedure: Select the good and bad unit Determine the quantitative parameter by which to measure the unitsDissemble the good unit – reassemble and measure it again. Disassemble and reassemble then measure the bad units again. If the difference D between good and bad exceeds the d difference (within units) by 5:1, a significant and repeatable difference between good and bad units is established
36Procedure (cont.)Based on engineering judgment, rank the likely component problems, within a unit, in descending order of perceived importance.Switch the top ranked component from the good unit to the bad unit or assembly with the corresponding component in the bad assembly going to the good assembly. Measure the 2 (reassembled) units.If there is no change: the good unit stays good bad stays bad, the top guessed component (A) is unimportant – go on to component BIf there is a partial change in the two measurements A is not the only important variable. A could be a Pink X family. Go on to Component BIf there is a complete reversal in outputs of the assemblies, A could be in the Red X family. There is no further need for components search.
37Procedure (cont.)Regardless of which of the three outcomes above are observed, restore component A to the original units to assure original conditions are repeated. Then, repeat the previous 2 steps for the next most important components: B, C, D, etc. if each swap leads to ‘no’ or ‘partial’ changeUltimately, the Red X family will be ID’d (on complete reversal) or two or more Pink X or pale Pink X families if only partial reversals are observed
38Procedure (cont.)With the important variables identified, a ‘capping run’ with the variables banded together as good or bad assemblies must be used to verify their importanceFinally, a factorial matrix, using data generated during the search, is drawn to determine, quantitatively, main effects and interactive effects.
39Paired ComparisonsThis is a technique like components search – but when products do not lend themselves to disassembly (perhaps it is a component in a component search!)Requires that there be several Good and Bad units that can be comparedRequires that a suitable parameter can be identified to distinguish Good from Bad
40Steps in Paired Comparison Randomly select one “Good” and one “Bad” unit – call it pair oneObserve the differences between the 2 units – these can be visual, dimensional, electrical, mechanical, chemical, etc. Observe using appropriate means (eye, optical or electron microscopic, X-ray, Spectrographic, tests-to-failure, etc)Select a 2nd pair, observe and note as with pair 1.Repeat with additional pairs until a pattern of repeatability is observed between “goods & bads”
41Reviewing:The previous (three methods) are ones that followed directly from Shainin’s “talk to the animals (products)” approachIn each, before we began actively specifying the DOE parameters, we collect as much information as we can from good or bad productsAs stated by one user: “The product solution was sought for over 18 months, we talked to engineers & designers; we talked to engineering managers, even product suppliers – all without a successful solution, but we never talked to the parts. With the component search technique we identified the problem in just 3 days”
42Taking the Next step: Variables Search The objective is toPinpoint the Red X, Pink X and one to three (more) critical interacting variablesIts possible that the ‘Red X’ is due to strong interactions between two or more variablesFinally we are still trying to separate the important variables from unimportant onesVariables search is a way to get statistically significant results without executing a large number of experimental runs (achieving knowledge at reduced cost)It has been shown the this binary comparison technique (on 5 to 15 variables) can be successful in 20, 22, 24 or 26 runs vs. 256, 512, 1024, etc. runs using traditional DOE
43Variables Search is a 2 stage process: List the important input variables as chosen by engineering judgment (in descending order of ability to influence output)Assign 2 levels to each factor – a best and worst level (within reasonable bounds)Run 2 experiments, one with all factors at best levels, the second with all factors at worst levels. Run two replications setsApply the D:d 5:1 rule (as above)If the 5:1 ratio is exceeded, the Red X is captured in the factor set tested.
44Stage 1 (cont):If the ratio is less than 5:1, the right factors are not chosen or 1 or more factors have been reversed between “best” & “worst” levels. Disappointing, but not fatal!If the wrong factors were chosen – in opinion of design team – decide on new factors and rerun Stage 1If the team believes it has the correct factors included, but some have reversed levels, run B vs. C tests on each suspicious factor to see if factor levels are in fact reversedOne could try the selected factors (4 at a time) using full factorial experiments – could be prone to failure too if interacting factors are separated during testing!
45Moving on to Stage 2:Run an experiment with AW (a at worst level) and the rest of factors at best levels (RB)If there is no change in best results in Stage 1 step 3, factor A is in fact unimportantIf there is a partial change from best results – toward Worst results – A is not the only important factor. A could be Pink XIf a complete reversal in Best to Worst results in Stage 1 step 3, A is the Red XRun a second test with AB and RWIf no change from Worst results in Stage 1 the top factor A is further confirmed as unimportantIf there is a partial change in the worst results in Stage 1 – toward Best results – A is further confirmed as a possible Pink X factorIf a complete reversal – Best results in Stage 1 are approximated, A is reconfirmed as the Red X
46Continuing Stage 2:Perform the same component search swap of step 1 & 2 for the rest of the factors to separate important from unimportant factorsIf no single Red X factor, but two or three Pink X factors are found, perform a capping or validation experiment with the Pink X’s at the best levels (remaining factors at their worst levels). The results should approximate the best results of Step 3, Stage 1.Run a second capping experiment with Pink’s at worst level, the rest at Best level – should approx. the worst results in Step 3, Stage 1.
47Variables Search Example: Press Brake Operation A press brake was showing high variability with poor CPKThe Press Brake was viewed as a “Black Magic” operation – the worked sometimes then went bad ‘for no reason’Causes of the operational variability were hotly debated, Issues included:Raw Sheet metalThicknessHardnessPress Brake Factors (some which are difficult or impossible to control)The company investigated new P. Brakes but observed no realistic and reliable improvementsEven high cost automated brakes sometimes produced poor results!
48A Variables Search was Performed Goal was to consistently achieve a .005” tolerance (or closer!)6 Factors were chosen:A. Punch/Die Alignment – B: ‘Aligned’, W: ‘not Specially Aligned’B. Metal Thickness – B: ‘Thick’, W: ‘Thin’C. Metal Hardness – B: ‘Hard’, W: ‘Soft’D. Metal Bow – B: ‘Flat’, W: ‘Bowed’E. Ram Storage – B: ‘Coin Form’, W: ‘Air Form’F. Holding Material – B: ‘Level’, W: ‘Angle’Results reported in “Process Widths” which is twice tolerance, in 0.001” units
49Results: STAGE 1 Process Width (x.001) All Best All Worst Initial 4 47 Rep 161D = 50; d = 7 D:d 7:1 (> 5:1) so a significant repeatable difference; Red X (or Pink X’s) captured as a factor
50Continuing to Stage 2 Test Comb. Results Conclusion 1 AWRB 3 A. not Important2ABRW102BWRB5B. Not Important4BBRW47CWRB7C. Not Important6CBRW72DWRB23Pink X: Interaction w/ other factor(s)8DBRW309EWRB???10EBRW2011FWRB73Prob. Red X + Interaction12FBRW18Cap RunDW FW RB70Complete Reversal EffectedDB FB RW
53Factorial Analysis:Factor G is Red X: It has a 41.9 main effect on the process spreadFactor D is a Pink X with 10.9 main effect on process spreadTheir interaction is minor with a contribution of 4.9 to process spreadWith D & F controlled, using a holding fixture to assure level and reduction in bowing (but with hardness and thickness tolerances open up leading to reduced raw metal costs) the process spread was reduced to 0.004” (.002) much better than the original target of .005” with an observed CPK of 2.5!
54Introduction to Failure Mode and Effects Analysis (FMEA) Tool used to systematically evaluate a product, process, or systemDeveloped in 1950’s by US Navy, for use with flight control systemsToday it’s used in several industries, in many applicationsproductsprocessesequipmentsoftwareserviceConducted on new or existing products/processesPresentation focuses on FMEA for existing process
55Benefits of FMEA Collects all potential issues into one document Can serve as troubleshooting guideIs valuable resource for new employees at the processProvides analytical assessment of process riskPrioritizes potential problems at processTotal process risk can be summarized, and compared to other processes to better allocate resourcesServes as baseline for future improvement at processActions resulting in improvements can be documentedPersonnel responsible for improvements can gain recognitionControls can be effectively implementedExample: Horizontal Bond Process: FM’s improved by 40%; causes improved by 37%. Overall risk in half in about 3 months.
56FMEA Development Assemble a team of people familiar with process Brainstorm process/product related defects (Failure Modes)List Effects, Causes, and Current Controls for each failure modeAssign ratings (1-10) for Severity, Occurrence, and Detection for each failure mode1 is best, 10 is worstDetermine Risk Priority Number (RPN) for each failure modeCalculated as Severity x Occurrence x Detection
58Capturing The Essence of FMEA The FMEA is a tool to systematically evaluate a process or productUse this methodology to:Prioritize which processes/ parameters/ characteristics to work on (Plan)Take action to improve process (Do)Implement controls to verify/validate process (Check)Update FMEA scores, and start focusing on next highest FM or cause (Act Plan)